Abstract
The hysteretic nonlinearity of pneumatic artificial muscle (PAM) is the main factor that degrades its tracking accuracy. This paper proposes an efficient hysteresis compensation method based on the active modeling control (AMC). Firstly, the Bouc–Wen model is adopted as the reference model to describe the hysteresis of the PAM. Secondly, the modeling errors are introduced into the reference model, and the unscented Kalman filter is used to estimate the state of the system and the modeling errors. Finally, a hysteresis compensation strategy is designed based on AMC. The compensation performances of the nominal controller with without AMC were experimentally tested on a PAM. The experimental results show that the proposed controller is more robust when tracking different types of trajectories. In the transient, both the overshoot and oscillation can be successfully attenuated, and fast convergence is achieved. In the steady-state, the proposed controller is more robust against external disturbances and measurement noise. The proposed controller is effective and robust in hysteresis compensation, thus improving the tracking performance of the PAM.
Highlights
Pneumatic artificial muscle (PAM) is a widely-utilized bionic flexible actuator
PAM is used in the exoskeleton system, and a terminal sliding mode control was adopted to higher trajectory tracking accuracy [1]
This paper proposes a hysteresis compensation scheme based on active model control (AMC), where the Bouc–Wen model is used to describe the hysteresis characteristics of the PAM
Summary
Pneumatic artificial muscle (PAM) is a widely-utilized bionic flexible actuator. PAM is mainly composed of a hollow rubber tube, a fiber woven mesh and metal connectors. This paper proposes a hysteresis compensation scheme based on active model control (AMC), where the Bouc–Wen model is used to describe the hysteresis characteristics of the PAM. The Bouc–Wen model for the PAM can be expressed as follows: where u is a generalized input, h is the hysteresis state variable, α, β and γ are the gains cfmrooonmdtreolellslaitnrsugticctthuteoresp.hlTaaphsietsicoisfretahslspeoohnaydsseto e.YprdtedI(entshtd)isa==iplnαokpoρtldhdpuicuit(saat−n)tpi+dβaopnbmddes+utr,c.kmoh(Fn1o−ct−rraγoρtnhl)sddehbutt(ePhtA)ehseMstmtioonov1tehssntoiegsaasstoetdfotihsniemtthrpiaslnifpsyiatpti(oh2ener), the control voltage applied to the proportional pressure regulator valve is the input of the overall system. The control voltage is adopted as the input in the Bouc–Wen model. Where Rk is the covariance matrix of the measurement noise, Υχ(k|k−1) is the measurement prediction of χk−1 from time k − 1 to time k, Kk is the Kalman gain, Xk|k is the estimation of state vector based on confidence field χk|k−1 and Pk|k is the update of confidence matrix Pk−1|k−1.
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